Bibliographic record
Abstract
I n Canada, as in other countries, there is an enormous problem of access to justice.Many courts are clogged, and people who have to rely on them for their cases to be heard are often required to wait for an unreasonably long time.Since access to justice is not entirely exempt from market forces, it is often prohibitively expensive for those who need it most.Like access to health care, which probably causes more ink to flow, access to justice is a major issue of distributive justice. 1 One of the justifications for introducing virtual platforms into the administration of justice is the claim that it could help to alleviate this major distributive justice problem."Cyberjustice" would shrink costs and waiting times related to justice proceedings by relieving congestion in the courts, reducing costs related to the need to pay various types of workers in the legal field, and so on.In the present essay, I will not try to challenge these claims.Let us therefore take it for granted that cyberjustice would entail major improvements in access to justice.Instead, I would like to look at the risks that could flow from overuse of virtual tools in the legal context.I am starting from the hypothesis that the design of any complex social institution has to take a multitude of values into account, values that are sometimes in tension.While use of virtual platforms may be an improvement in terms of access to justice, does it entail risks in relation to other values that are just as central for legal institutions, risks that could significantly reduce the overall benefit brought about by the introduction of new technologies?
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.062 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".